CS 5522: Artificial Intelligence II Adversarial Search Instructor: Alan Ritter Ohio State University [These slides were adapted from CS188 Intro to AI at UC Berkeley. All materials available at http://ai.berkeley.edu.]
Game Playing State-of-the-Art
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved!
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved!
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic.
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic.
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Go: Human champions are now starting to be challenged by machines, though the best humans still beat the best machines. In go, b > 300! Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods.
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Go: Human champions are now starting to be challenged by machines, though the best humans still beat the best machines. In go, b > 300! Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods.
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Go: Human champions are now starting to be challenged by machines, though the best humans still beat the best machines. In go, b > 300! Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods.
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Go: Human champions are now starting to be challenged by machines, though the best humans still beat the best machines. In go, b > 300! Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods. Pacman
Game Playing State-of-the-Art Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-yearreign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved! Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Go: Human champions are now starting to be challenged by machines, though the best humans still beat the best machines. In go, b > 300! Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods. Pacman
Behavior from Computation [Demo: mystery pacman (L6D1)]
Adversarial Games
Types of Games Many different kinds of games! Axes: Deterministic or stochastic? One, two, or more players? Zero sum? Perfect information (can you see the state)? Want algorithms for calculating a strategy (policy) which recommends a move from each state
Deterministic Games Many possible formalizations, one is: States: S (start at s 0 ) Players: P={1...N} (usually take turns) Actions: A (may depend on player / state) Transition Function: SxA S Terminal Test: S {t,f} Terminal Utilities: SxP R Solution for a player is a policy: S A
Zero-Sum Games Zero-Sum Games Agents have opposite utilities (values on outcomes) Lets us think of a single value that one maximizes and the other minimizes Adversarial, pure competition General Games Agents have independent utilities (values on outcomes) Cooperation, indifference, competition, and more are all possible More later on non-zero-sum games
Adversarial Search
Single-Agent Trees
Single-Agent Trees
Single-Agent Trees
Single-Agent Trees
Single-Agent Trees 8
Single-Agent Trees 8 2 0 2 6 4 6
Value of a State 8 2 0 2 6 4 6
Value of a State Value of a state: The best achievable outcome (utility) from that state 8 2 0 2 6 4 6
Value of a State Value of a state: The best achievable outcome (utility) from that state 8 2 0 2 6 4 6 Terminal States:
Value of a State Value of a state: The best achievable outcome (utility) from that state Non-Terminal States: 8 2 0 2 6 4 6 Terminal States:
Adversarial Game Trees
Adversarial Game Trees
Adversarial Game Trees
Adversarial Game Trees
Adversarial Game Trees -20-8 -18-5 -10 +4-20 +8
Minimax Values -8-5 -10 +8
Minimax Values -8-5 -10 +8 Terminal States:
Minimax Values States Under Opponent s Control: -8-5 -10 +8 Terminal States:
Minimax Values States Under Agent s Control: States Under Opponent s Control: -8-5 -10 +8 Terminal States:
Tic-Tac-Toe Game Tree
Adversarial Search (Minimax) Deterministic, zero-sum games: Tic-tac-toe, chess, checkers One player maximizes result The other minimizes result Minimax search: A state-space search tree Players alternate turns Compute each node s minimax value: the best achievable utility against a rational (optimal) adversary max 8 2 5 6 min
Adversarial Search (Minimax) Deterministic, zero-sum games: Tic-tac-toe, chess, checkers One player maximizes result The other minimizes result Minimax search: A state-space search tree Players alternate turns Compute each node s minimax value: the best achievable utility against a rational (optimal) adversary max 8 2 5 6 Terminal values: part of the game min
Adversarial Search (Minimax) Deterministic, zero-sum games: Tic-tac-toe, chess, checkers One player maximizes result The other minimizes result Minimax search: A state-space search tree Players alternate turns Compute each node s minimax value: the best achievable utility against a rational (optimal) adversary Minimax values: computed recursively max 8 2 5 6 Terminal values: part of the game min
Adversarial Search (Minimax) Deterministic, zero-sum games: Tic-tac-toe, chess, checkers One player maximizes result The other minimizes result Minimax search: A state-space search tree Players alternate turns Compute each node s minimax value: the best achievable utility against a rational (optimal) adversary Minimax values: computed recursively max 2 5 8 2 5 6 Terminal values: part of the game min
Adversarial Search (Minimax) Deterministic, zero-sum games: Tic-tac-toe, chess, checkers One player maximizes result The other minimizes result Minimax search: A state-space search tree Players alternate turns Compute each node s minimax value: the best achievable utility against a rational (optimal) adversary Minimax values: computed recursively 5 max 2 5 8 2 5 6 Terminal values: part of the game min
Minimax Implementation def max-value(state): initialize v = - for each successor of state: v = max(v, min-value(successor)) return v
Minimax Implementation def max-value(state): initialize v = - for each successor of state: v = max(v, min-value(successor)) return v def min-value(state): initialize v = + for each successor of state: v = min(v, max-value(successor)) return v
Minimax Implementation def max-value(state): initialize v = - for each successor of state: v = max(v, min-value(successor)) return v def min-value(state): initialize v = + for each successor of state: v = min(v, max-value(successor)) return v
Minimax Implementation def max-value(state): initialize v = - for each successor of state: v = max(v, min-value(successor)) return v def min-value(state): initialize v = + for each successor of state: v = min(v, max-value(successor)) return v
Minimax Implementation (Dispatch) def value(state): if the state is a terminal state: return the state s utility if the next agent is MAX: return max-value(state) if the next agent is MIN: return min-value(state)
Minimax Implementation (Dispatch) def value(state): if the state is a terminal state: return the state s utility if the next agent is MAX: return max-value(state) if the next agent is MIN: return min-value(state) def max-value(state): initialize v = - for each successor of state: v = max(v, value(successor)) return v def min-value(state): initialize v = + for each successor of state: v = min(v, value(successor)) return v
Minimax Example
Minimax Example
3 Minimax Example
Minimax Example 3 12
Minimax Example 3 12 8
Minimax Example 3 12 8
3 12 8 2 Minimax Example
Minimax Example 3 12 8 2 4
Minimax Example 3 12 8 2 4 6
Minimax Example 3 12 8 2 4 6
Minimax Example 3 12 8 2 4 6 14
Minimax Example 3 12 8 2 4 6 14 5
Minimax Example 3 12 8 2 4 6 14 5 2
Minimax Efficiency How efficient is minimax? Just like (exhaustive) DFS Time: O(b m ) Space: O(bm) Example: For chess, b 35, m 100 Exact solution is completely infeasible But, do we need to explore the whole tree?
Minimax Properties max min 10 10 9 100 Optimal against a perfect player. Otherwise? [Demo: min vs exp (L6D2, L6D3)]
Minimax Properties max min 10 10 9 100 Optimal against a perfect player. Otherwise? [Demo: min vs exp (L6D2, L6D3)]
Minimax Properties max min 10 10 9 100 Optimal against a perfect player. Otherwise? [Demo: min vs exp (L6D2, L6D3)]
Video of Demo Min vs. Exp (Min)
Video of Demo Min vs. Exp (Min)
Video of Demo Min vs. Exp (Min)
Video of Demo Min vs. Exp (Exp)
Video of Demo Min vs. Exp (Exp)
Video of Demo Min vs. Exp (Exp)
Resource Limits
Resource Limits Problem: In realistic games, cannot search to leaves! max min????
Resource Limits Problem: In realistic games, cannot search to leaves! Solution: Depth-limited search Instead, search only to a limited depth in the tree Replace terminal utilities with an evaluation function for nonterminal positions max min????
Resource Limits Problem: In realistic games, cannot search to leaves! Solution: Depth-limited search Instead, search only to a limited depth in the tree Replace terminal utilities with an evaluation function for nonterminal positions -1-2 4 9 max min????
Resource Limits Problem: In realistic games, cannot search to leaves! Solution: Depth-limited search Instead, search only to a limited depth in the tree Replace terminal utilities with an evaluation function for nonterminal positions 4-2 4-1 -2 4 9 max min????
Resource Limits Problem: In realistic games, cannot search to leaves! Solution: Depth-limited search Instead, search only to a limited depth in the tree Replace terminal utilities with an evaluation function for nonterminal positions Example: Suppose we have 100 seconds, can explore 10K nodes / sec So can check 1M nodes per move α-β reaches about depth 8 decent chess program 4-2 4-1 -2 4 9 max min????
Resource Limits Problem: In realistic games, cannot search to leaves! Solution: Depth-limited search Instead, search only to a limited depth in the tree Replace terminal utilities with an evaluation function for nonterminal positions Example: Suppose we have 100 seconds, can explore 10K nodes / sec So can check 1M nodes per move α-β reaches about depth 8 decent chess program Guarantee of optimal play is gone More plies makes a BIG difference Use iterative deepening for an anytime algorithm 4-2 4-1 -2 4 9???? max min
Depth Matters Evaluation functions are always imperfect The deeper in the tree the evaluation function is buried, the less the quality of the evaluation function matters An important example of the tradeoff between complexity of features and complexity of computation [Demo: depth limited (L6D4, L6D5)]
Video of Demo Limited Depth (2)
Video of Demo Limited Depth (2)
Video of Demo Limited Depth (2)
Video of Demo Limited Depth (10)
Video of Demo Limited Depth (10)
Video of Demo Limited Depth (10)
Evaluation Functions
Evaluation Functions Evaluation functions score non-terminals in depth-limited search Ideal function: returns the actual minimax value of the position In practice: typically weighted linear sum of features: e.g. f 1 (s) = (num white queens num black queens), etc.
Evaluation for Pacman [Demo: thrashing d=2, thrashing d=2 (fixed evaluation function), smart ghosts coordinate
Evaluation for Pacman [Demo: thrashing d=2, thrashing d=2 (fixed evaluation function), smart ghosts coordinate
Evaluation for Pacman [Demo: thrashing d=2, thrashing d=2 (fixed evaluation function), smart ghosts coordinate
Evaluation for Pacman [Demo: thrashing d=2, thrashing d=2 (fixed evaluation function), smart ghosts coordinate
Evaluation for Pacman [Demo: thrashing d=2, thrashing d=2 (fixed evaluation function), smart ghosts coordinate
Video of Demo Thrashing (d=2)
Video of Demo Thrashing (d=2)
Video of Demo Thrashing (d=2)
Why Pacman Starves A danger of replanning agents! He knows his score will go up by eating the dot now (west, east) He knows his score will go up just as much by eating the dot later (east, west) There are no point-scoring opportunities after eating the dot (within the horizon, two here) Therefore, waiting seems just as good as eating: he may go east, then back west in the next round of replanning!
Why Pacman Starves A danger of replanning agents! He knows his score will go up by eating the dot now (west, east) He knows his score will go up just as much by eating the dot later (east, west) There are no point-scoring opportunities after eating the dot (within the horizon, two here) Therefore, waiting seems just as good as eating: he may go east, then back west in the next round of replanning!
Why Pacman Starves A danger of replanning agents! He knows his score will go up by eating the dot now (west, east) He knows his score will go up just as much by eating the dot later (east, west) There are no point-scoring opportunities after eating the dot (within the horizon, two here) Therefore, waiting seems just as good as eating: he may go east, then back west in the next round of replanning!
Why Pacman Starves A danger of replanning agents! He knows his score will go up by eating the dot now (west, east) He knows his score will go up just as much by eating the dot later (east, west) There are no point-scoring opportunities after eating the dot (within the horizon, two here) Therefore, waiting seems just as good as eating: he may go east, then back west in the next round of replanning!
Video of Demo Smart Ghosts (Coordination)
Video of Demo Smart Ghosts (Coordination)
Video of Demo Smart Ghosts (Coordination)
Video of Demo Smart Ghosts (Coordination) Zoomed In
Video of Demo Smart Ghosts (Coordination) Zoomed In
Video of Demo Smart Ghosts (Coordination) Zoomed In
Game Tree Pruning
Minimax Example
Minimax Example
3 Minimax Example
Minimax Example 3 12
Minimax Example 3 12 8
Minimax Example 3 12 8
3 12 8 2 Minimax Example
Minimax Example 3 12 8 2 4
Minimax Example 3 12 8 2 4 6
Minimax Example 3 12 8 2 4 6
Minimax Example 3 12 8 2 4 6 14
Minimax Example 3 12 8 2 4 6 14 5
Minimax Example 3 12 8 2 4 6 14 5 2
Minimax Pruning
Minimax Pruning
3 Minimax Pruning
Minimax Pruning 3 12
Minimax Pruning 3 12 8
Minimax Pruning 3 12 8
3 12 8 2 Minimax Pruning
3 12 8 2 Minimax Pruning
Minimax Pruning 3 12 8 2 14
Minimax Pruning 3 12 8 2 14 5
Minimax Pruning 3 12 8 2 14 5 2
Alpha-Beta Pruning General configuration (MIN version) We re computing the MIN-VALUE at some node n We re looping over n s children n s estimate of the childrens min is dropping Who cares about n s value? MAX Let a be the best value that MAX can get at any choice point along the current path from the root If n becomes worse than a, MAX will avoid it, so we can stop considering n s other children (it s already bad enough that it won t be played) MAX version is symmetric MAX MIN MAX MIN a n
Alpha-Beta Implementation α: MAX s best option on path to root β: MIN s best option on path to root def max-value(state, α, β): initialize v = - for each successor of state: v = max(v, value(successor, α, β)) if v β return v α = max(α, v) return v def min-value(state, α, β): initialize v = + for each successor of state: v = min(v, value(successor, α, β)) if v α return v β = min(β, v) return v
Alpha-Beta Pruning Properties This pruning has no effect on minimax value computed for the root! Values of intermediate nodes might be wrong Important: children of the root may have the wrong value So the most naïve version won t let you do action selection max min 10 10 0
Alpha-Beta Pruning Properties This pruning has no effect on minimax value computed for the root! Values of intermediate nodes might be wrong Important: children of the root may have the wrong value So the most naïve version won t let you do action selection max Good child ordering improves effectiveness of pruning With perfect ordering : Time complexity drops to O(b m/2 ) Doubles solvable depth! Full search of, e.g. chess, is still hopeless 10 10 0 min This is a simple example of metareasoning (computing about what to compute)